Authors:
M. Saquib Sarfraz
and
Olaf Hellwich
Affiliation:
Computer vision and Remote Sensing, Berlin university of Technology, Germany
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Feature Extraction
;
Features Extraction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
;
Statistical Approach
Abstract:
Recognizing a face from a novel view point poses major challenges for automatic face recognition. Recent methods address this problem by trying to model the subject specific appearance change across pose. For this, however, almost all of the existing methods require a perfect alignment between a gallery and a probe image. In this paper we present a pose invariant face recognition method centered on modeling joint appearance of gallery and probe images across pose in a probabilistic framework. We propose novel extensions in this direction by introducing to use a more robust feature description as opposed to pixel-based appearances. Using such features we put forward to synthesize the non-frontal views to frontal. Furthermore, using local kernel density estimation, instead of commonly used normal density assumption, is suggested to derive the prior models. Our method does not require any strict alignment between gallery and probe images which makes it particularly attractive as compare
d to the existing state of the art methods. Improved recognition across a wide range of poses has been achieved using these extensions.
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